simple relevance chicago ama big date presentation deck

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Page 1: Simple Relevance CHICAGO AMA BIG DATE presentation deck
Page 2: Simple Relevance CHICAGO AMA BIG DATE presentation deck

We Make Your Digital Marketing Awesome & Lucrative

Create More Revenue

a lot

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Erik Severinghaus CEOLong time in Email(Founding team for iContact)

Big Data NerdPatent #20080320482A1“Management of Grid Computing Resources Based on Service Level Environments”

Marketing KnowledgeKellogg MBA

About The Speaker:

[email protected] | 312-569-9431 | @SimpleRelevance

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About SimpleRelevanceFounded by a co-founder of market-leading ESP iContact and Partner in IBM’s IT Optimization consulting team with 10+ years experience in digital marketing optimization.

Funded by top Venture Capitalists, incubated at the 1871 technology center in Chicago.

Patent-pending algorithm from University of Chicago researchers developed over 2+ years.

Market leading industry partners & Fortune 500 clients.

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0

12.5

25

37.5

50

Mobile Social Display Search EmailReturn on Investment per $1 Spent

Email is Still King

Source: Direct Marketing Association

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Politics Sports Marketing

The World Is Changing

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Math Men

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Accounting 101Income Statement How much money did you make?

Cash Flows Statement How much cash did that generate?

Balance Sheet What are your assets and liabilities?

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The Balance SheetYour email list is probably worth millions of dollars. You should treat it like a valuable asset.

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Mirrors Ecology

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Business World

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Understand The Value

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The Challenge

marketers can’t make data actionable for individuals

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Most Email Is:

Batch & BlastMaybe A|B Testing

Some SegmentationReally Manual (“Hard”)

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Email Should Be:

Personalized Totally Data Driven

Automated (“Simple”)

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Data

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Math You Like

Personalized Totally Data Driven

Automated (“Simple”)

30-300% Improvement in Email Metrics

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The Process

Segment at the Individual Level to improve messaging and conversion with personalization

Close the Loop using data to continuously, automatically optimize campaign with a iteration cycle measured in minutes

Define Dynamic Workflows to move prospects through the funnel

Reporting Deploy Campaign Setup Campaign Identify Segment

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Full Loop Marketing

crm profiles social shares, likes, profile info, email opens &clicks cart purchases and clicks

First Party Data

Third Party Data

auto content creation, demographics,other metadata

Taking Action

optimized, automated,

emails, tweets and other marketing

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Use Cases

Optimize all email sends for time of day based on likelihood of conversion.

Optimize all email subject lines for likelihood to open.

Optimize creative, location, subject line, and time of day personalization for cross-sell messaging.

Optimize offers, creative, subject line, and time of day targeting for upsell communication.

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Profile Li-Ning is the third largest sportswear company in the world, a market leader in Asia and emerging brand in the United States receiving endorsements from superstars such as Dwayne Wade.

ChallengeWhen Li-Ning launched in the U.S. they put tremendous emphasis on establishing a relationship with their customers. Because they were d i ff e r e n t , L i - N i n g w a n t e d t o personalize their experience.

SolutionAfter launching, Li-Ning introduced SimpleRelevance data personalization into their email campaigns

52% increase in revenue20+% AOV increases

Improvement

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Profile The johnnie-O line has evolved into an apparel and lifestyle brand distinctly defined as "west coast prep". Items for men, women, and children include shirts, belts, hats, socks, sweatshirts, and specialty items.

Challengejohnnie-O wanted to increase online sales by sending data driven, personalized email marketing to its customers

Solutionj o h n n i e - O i m p l e m e n t e d S i m p l e R e l e v a n c e ’ s p r o d u c t recommendation engine into their branded emails

Improvement40-50% increase in revenue per email campaignSimpleRelevance quickly became johnnie-O’s most successful marketing strategy from an ROI perspective

Improvement 40-50% increase in revenue per email campaign

SimpleRelevance quickly became johnnie-O‘s most successful marketing strategy from an ROI perspective

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Profile A successful Fortune 500 car manufacturer with headquarters overseas. Produces standard as well as luxury models.

ChallengeT h e m a n u f a c t u r e r w a n t e d t o incorporate SimpleRelevance’s data analytics into their social media marketing, specifically Twitter, to influence customer ’s purchase decisions when car shopping.

SolutionSimpleRelevance created tailored landing pages and integrated smart analyt ics into a custom twit ter m o n i t o r i n g t o o l t h a t a l l o w e d SimpleRelevance to deliver custom, targeted tweets to users who had tweeted about car shopping.

Improvement $160,000 in sales in the first month

“Those who get an offer [from SimpleRelevance] closed 2x more often than those who didn’t.“ – VP of Marketing

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Get In Touch

Email: [email protected]: 202-431-5699Office: 312-569-9431 Twitter: @SimpleRelevance

http://www.simplerelevance.com

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How It WorksThe SimpleRelevance engine tracks and analyzes every interaction between each customer and your email marketing.

For each message, we track a plethora of data points including: Time Of Day and Day of Week “From” Address, Subject Line, Word Count What Creative & Content Was Included What Links Were Clicked

We correlate those responses with each customer, as well as the existing demographic and psychographic data to build a predictive model for each individual customer.

We continuously learn with every interaction, so the system gets smarter with time.

Because the algorithms do the optimization, you don’t need to do it all manually.

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Bayesian LearningThe system is constantly evaluating each of the data points that are integrated into our system, looking for both data points that resonate across segments of users as well as predictive elements that stand out for certain users.

With each new data point, the entire system becomes smarter.

The engine regenerates itself nightly, evaluating new data and updating propensity models to take into account new information.

The system can be tuned for different types of interactions and objects, optimizing on any number of variables so long as metadata is structured coherently.